Data mining can be an effective tool for making better hires,
giving employers the ability to find new and unexpected relationships in the
numbers. But there continues to be times when human judgment has to be factored
in as well.

The
Wall Street Journal recently published an article about the rise of
"Big Data" in human resources, especially in hiring, and the story
seemed to touch a nerve.

Big
Data refers to the ability to look for relationships
in big data sets, like the items bought by thousands of consumers in grocery
stores over a year. Especially in human resources -- where the size of the data
sets is much, much smaller -- a more accurate phrase for the new developments
is "data mining," where we are fishing for relationships in data
without a lot of guidance as to where to look.

Most of us have grown up with hiring processes that seemed pretty
informal: A personal contact leading to some interviews and then to a job
offer, with little or no data analysis involved. So it is surprising to hear
that this was not always the case. A generation ago, a candidate for a
white-collar job in a big company would have found themselves going through
days of testing -- IQ tests, skills tests, interviews with psychiatrists, you
name it. That died off, in part because employers were no longer making
lifetime hiring decisions, so the benefits of careful screening weren't so big,
and in part because the new threat of being sued for discriminatory hiring
practices. Companies backed away from formal hiring practices that were easy to
test for discrimination in favor of informal practices that were more difficult
to track.

Now a lot of those hiring tests are making a comeback, in part
because they can be done online at significantly lower cost. Perhaps
surprisingly, the interest has begun not with management jobs but at the lower
end of the labor market, in call centers. One reason why is that these call
centers hire so many people, largely because of high turnover, that it could
pay to put in place the initially expensive screening systems. The other reason
is that it is relatively easy to track individual performance in these jobs,
which in turn makes it easier to use data-mining techniques to determine what
constitutes good performance.

This
approach is fundamentally different from the more common computerized
application systems. The latter filter out the online candidates who do not
have the basic qualifications for a job, and then turns those who do over to
recruiters for a closer look. In practice, though, there is very little real
evidence motivating those systems. The criteria used for the filters typically
come from the gut hunches of hiring managers (e.g., "We need someone with
a Ph.D for this job.")

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Data
mining is different because it is asking the empirical question, What actually
does predict who turns out to be a good hire? Some of this is being done by
companies studying themselves, Google being the most famous example when it
discovered that their previous practices were not that successful. But most of
it is being done by vendors, especially recruiting-process outsourcers.

What the data mining is finding is in part what those recruiting
and staffing experts from the 1960s already knew. Beyond the usual
qualifications and experiences, factors like IQ and personality can help find
the better candidates. But they are also discovering new relationships that
were not expected by hiring experts. One vendor, Evolve, found that job hopping
among candidates was not a bad thing. Candidates who had changed jobs a lot
were not more likely to change jobs when hired into the call centers they were
managing. Why would you not want to know information like this?

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There
is always a risk of messing this up, of course. The easiest way to do it would
be for employers to rely on only one result from data mining, such as only
using IQ to assess candidates. In practice, even the best of these statistical
relationships account for only a small amount of candidate success. A lot of
information is needed to make good hires; and a lot of human judgment is
required as well, especially when the different pieces of information do not
point in the same direction.

But
looking at actual relationships between individual attributes and performance
is a big step beyond going with the gut feelings of hiring managers and
incorporating them into software.

We may
like the idea that hiring should be about the plucky applicant who persuades a
human recruiter to hire them. But with thousands of applicants for most every
position and a lot of money on the line, it is not surprising that employers
are once again turning to more sophisticated analyses to solve the problem.